人脸识别文献翻译中英双文Word文档格式.docx

1、4.1 Feature LocalizationBefore discussing the methods of comparing two facial images we now take a brief look at some at the preliminary processes of facial feature alignment. This process typically consists of two stages: face detection and eye localization. Depending on the application, if the pos

2、ition of the face within the image is known beforehand （for a cooperative subject in a door access system for example） then the face detection stage can often be skipped, as the region of interest is already known. Therefore, we discuss eye localization here, with a brief discussion of face detectio

3、n in the literature review .The eye localization method is used to align the 2D face images of the various test sets used throughout this section. However, to ensure that all results presented are representative of the face recognition accuracy and not a product of the performance of the eye localiz

4、ation routine, all image alignments are manually checked and any errors corrected, prior to testing and evaluation.We detect the position of the eyes within an image using a simple template based method. A training set of manually pre-aligned images of faces is taken, and each image cropped to an ar

5、ea around both eyes. The average image is calculated and used as a template.Figure 4-1 The average eyes. Used as a template for eye detection.Both eyes are included in a single template, rather than individually searching for each eye in turn, as the characteristic symmetry of the eyes either side o

6、f the nose, provide a useful feature that helps distinguish between the eyes and other false positives that may be picked up in the background. Although this method is highly susceptible to scale （i.e. subject distance from the camera） and also introduces the assumption that eyes in the image appear

7、 near horizontal. Some preliminary experimentation also reveals that it is advantageous to include the area of skin just beneath the eyes. The reason being that in some cases the eyebrows can closely match the template, particularly if there are shadows in the eye-sockets, but the area of skin below

8、 the eyes helps to distinguish the eyes from eyebrows （the area just below the eyebrows contain eyes, whereas the area below the eyes contains only plain skin）.A window is passed over the test images and the absolute difference taken to that of the average eye image shown above. The area of the imag

9、e with the lowest difference is taken as the region of interest containing the eyes. Applying the same procedure using a smaller template of the individual left and right eyes then refines each eye position.This basic template-based method of eye localization, although providing fairly precise local

10、izations, often fails to locate the eyes completely. However, we are able to improve performance by including a weighting scheme.Eye localization is performed on the set of training images, which is then separated into two sets: those in which eye detection was successful; and those in which eye det

11、ection failed. Taking the set of successful localizations we compute the average distance from the eye template （Figure 4-2 top）. Note that the image is quite dark, indicating that the detected eyes correlate closely to the eye template, as we would expect. However, bright points do occur near the w

12、hites of the eye, suggesting that this area is often inconsistent, varying greatly from the average eye template. Figure 4-2 Distance to the eye template for successful detections （top） indicating variance due to noise and failed detections （bottom） showing credible variance due to miss-detected fea

13、tures.In the lower image （Figure 4-2 bottom）, we have taken the set of failed localizations（images of the forehead, nose, cheeks, background etc. falsely detected by the localization routine） and once again computed the average distance from the eye template. The bright pupils surrounded by darker a

14、reas indicate that a failed match is often due to the high correlation of the nose and cheekbone regions overwhelming the poorly correlated pupils. Wanting to emphasize the difference of the pupil regions for these failed matches and minimize the variance of the whites of the eyes for successful mat

15、ches, we divide the lower image values by the upper image to produce a weights vector as shown in Figure 4-3. When applied to the difference image before summing a total error, this weighting scheme provides a much improved detection rate.Figure 4-3 - Eye template weights used to give higher priorit

16、y to those pixels that best represent the eyes.4.2 The Direct Correlation ApproachWe begin our investigation into face recognition with perhaps the simplest approach, known as the direct correlation method （also referred to as template matching by Brunelli and Poggio） involving the direct comparison

17、 of pixel intensity values taken from facial images. We use the term Direct Correlation to encompass all techniques in which face images are compared directly, without any form of image space analysis, weighting schemes or feature extraction, regardless of the distance metric used. Therefore, we do

18、not infer that Pearsons correlation is applied as the similarity function （although such an approach would obviously come under our definition of direct correlation）. We typically use the Euclidean distance as our metric in these investigations （inversely related to Pearsons correlation and can be c

19、onsidered as a scale and translation sensitive form of image correlation）, as this persists with the contrast made between image space and subspace approaches in later sections.Firstly, all facial images must be aligned such that the eye centers are located at two specified pixel coordinates and the

20、 image cropped to remove any background information. These images are stored as grayscale bitmaps of 65 by 82 pixels and prior to recognition converted into a vector of 5330 elements （each element containing the corresponding pixel intensity value）. Each corresponding vector can be thought of as des

21、cribing a point within a 5330 dimensional image space. This simple principle can easily be extended to much larger images: a 256 by 256 pixel image occupies a single point in 65,536-dimensional image space and again, similar images occupy close points within that space. Likewise, similar faces are l

22、ocated close together within the image space, while dissimilar faces are spaced far apart. Calculating the Euclidean distance d, between two facial image vectors （often referred to as the query image q, and gallery image g）, we get an indication of similarity. A threshold is then applied to make the

23、 final verification decision.4.2.1 Verification TestsThe primary concern in any face recognition system is its ability to correctly verify a claimed identity or determine a persons most likely identity from a set of potential matches in a database. In order to assess a given systems ability to perfo

24、rm these tasks, a variety of evaluation methodologies have arisen. Some of these analysis methods simulate a specific mode of operation （i.e. secure site access or surveillance）, while others provide a more mathematical description of data distribution in some classification space. In addition, the

25、results generated from each analysis method may be presented in a variety of formats. Throughout the experimentations in this thesis, we primarily use the verification test as our method of analysis and comparison, although we also use Fishers Linear Discriminate to analyze individual subspace compo

26、nents in section 7 and the identification test for the final evaluations described in section 8. The verification test measures a systems ability to correctly accept or reject the proposed identity of an individual. At a functional level, this reduces to two images being presented for comparison, fo

27、r which the system must return either an acceptance （the two images are of the same person） or rejection （the two images are of different people）. The test is designed to simulate the application area of secure site access. In this scenario, a subject will present some form of identification at a po

28、int of entry, perhaps as a swipe card, proximity chip or PIN number. This number is then used to retrieve a stored image from a database of known subjects （often referred to as the target or gallery image） and compared with a live image captured at the point of entry （the query image）. Access is the

29、n granted depending on the acceptance/rejection decision. The results of the test are calculated according to how many times the accept/reject decision is made correctly. In order to execute this test we must first define our test set of face images. Although the number of images in the test set doe

30、s not affect the results produced （as the error rates are specified as percentages of image comparisons）, it is important to ensure that the test set is sufficiently large such that statistical anomalies become insignificant （for example, a couple of badly aligned images matching well）. Also, the ty

31、pe of images （high variation in lighting, partial occlusions etc.） will significantly alter the results of the test. Therefore, in order to compare multiple face recognition systems, they must be applied to the same test set.However, it should also be noted that if the results are to be representati

32、ve of system performance in a real world situation, then the test data should be captured under precisely the same circumstances as in the application environment. On the other hand, if the purpose of the experimentation is to evaluate and improve a method of face recognition, which may be applied to a range of application environments, then the test data should present the range of difficulties that are to be overcome. This may mean including a greater percentage of difficult images than would be expected in the perceived operating conditions and hence higher error rates in the results